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Acoustic features of dysphonic speech vs normal speech in New Zealand English speakers
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Modeling verb valency in a computational grammar for Portuguese in the HPSG formalism ; Modelação da valência verbal numa gramática computacional do português no formalismo HPSG
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In: Domínios de Lingu@gem; Ahead of Print; 1-63 ; 1980-5799 (2022)
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Causal and Semantic Relations in L2 Text Processing: An Eye-Tracking Study
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Nahatame, Shingo. - : University of Hawaii National Foreign Language Resource Center, 2022. : Center for Language & Technology, 2022
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Recognition of Urdu sign language: a systematic review of the machine learning classification
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In: PeerJ Comput Sci (2022)
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Abstract:
BACKGROUND AND OBJECTIVE: Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. MATERIALS AND METHODS: All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review. RESULTS: This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language. CONCLUSION: Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language.
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Keyword:
Computational Linguistics
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044266/ https://doi.org/10.7717/peerj-cs.883
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Multi-label emotion classification of Urdu tweets
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In: PeerJ Comput Sci (2022)
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Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache
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Word Frequency Analysis of Community Reaction to Religious Violence on Social Media
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In: School of Computer Science & Engineering Faculty Publications (2022)
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(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment
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In: PeerJ Comput Sci (2022)
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A systematic literature review on spam content detection and classification
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In: PeerJ Comput Sci (2022)
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People’s expectations and experiences of big data collection in the Saudi context
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In: PeerJ Comput Sci (2022)
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Developing and evaluating cybersecurity competencies for students in computing programs
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In: PeerJ Comput Sci (2022)
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Multitask Pointer Network for Multi-Representational Parsing
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CorpusExplorer ; Eine Software zur korpuspragmatischen Analyse
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Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Masked language models directly encode linguistic uncertainty
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Learning Stress Patterns with a Sequence-to-Sequence Neural Network
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Modeling human-like morphological prediction
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In: Proceedings of the Society for Computation in Linguistics (2022)
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The interaction between cognitive ease and informativeness shapes the lexicons of natural languages
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In: Proceedings of the Society for Computation in Linguistics (2022)
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What is so Plautine about Plautine Language? Computers and the Style of Early Latin Drama
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In: Peter Barrios-Lech (2022)
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